A Model of Proactive-Reactive Job Shop Scheduling to Tackle Uncertain Events with Greedy Randomized Adaptive Search Procedure

Muhammad Usman Nisar, Anas Ma'ruf, Andi Cakravastia, Abdul Hakim Halim

Abstract


Despite substantial research on job shop scheduling (JSS), there is a gap owing to the lack of a unified framework that considers exact, heuristic, and metaheuristic methods for JSS. This study addressed this gap by presenting a comprehensive approach. The study offered following contributions in this regard: analyzed the exact optimization method for benchmarking, investigated a greedy algorithm (G_r A) for faster solutions, and implemented a novel Greedy Randomized Adaptive Search Procedure (GRASP) to achieve high-quality solutions with computational effectiveness. Additionally, this study considered serious dynamic events (SDE) such as new job arrivals (NJA), rush order (RO), machine failures (MF), and scheduled machine maintenance (SMM), as scheduling disruptions and proposed a proactive-reactive rescheduling strategy, with right-shift (RF) and regeneration (Reg) methods using a hybrid (periodic and event-driven) policy to tackle them. Results showed that the exact methods are optimal but computationally intensive, G_r A are faster but suboptimal, and GRASP strike a balance, delivering high-quality solutions with only a 3.43% gap from exact methods while maintaining computational efficiency. Additionally, RF method effectively handled MF, while Reg efficiently integrated NJA, RO, and SMM. Overall, this study offered a comprehensive approach to JSS, enhancing applicability in manufacturing environments.


Keywords


Job Shop Scheduling; Dynamic Events; GRASP; Proactive-Reactive Rescheduling.

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References


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DOI: https://doi.org/10.18196/jrc.v5i6.22208

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Journal of Robotics and Control (JRC)

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